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Get one Performance Science Library evidence chain

get_evidence_source
Read-only

Get the full source → finding → construct → survey-item evidence chain for one source by id (from list_evidence_sources): its citation, every finding extracted from it, the constructs derived from each finding, and the survey items derived from each construct. Same content rendered at performix.app/learn/evidence/. HONESTY: the citation is exposed as-is, including the 132 sources still carrying a placeholder title (HO-2431) — citationPlaceholder says which.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesSource id, e.g. source.corpus.aguinis_2019 (from list_evidence_sources).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond the annotations: it discloses the honesty caveat about placeholder titles and the citationPlaceholder field, plus it notes the content matches a public URL. This is meaningful additional transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: the primary purpose and full content scope appear in the first sentence, and the important honesty caveat is cleanly separated in the second. Every clause earns its place, and there is no redundant repetition of the tool name or title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has one required parameter, a complete input schema, an output schema, and clear read-only annotations. The description fully explains what the agent will receive, how to identify the source, and even flags a known data-quality issue. Nothing needed for correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the id parameter is already well documented in the schema with a concrete example and provenance ('from list_evidence_sources'). The description reinforces this but does not add new parameter-level meaning beyond what the schema already provides, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Get') and a precise resource ('full source → finding → construct → survey-item evidence chain for one source by id'), and enumerates exactly what is included. It clearly differentiates this single-source retrieval tool from the sibling list tool list_evidence_sources by emphasizing 'one source by id'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly implies when to use this tool: when you need the complete evidence chain for a specific source, and it directs the user to obtain the id from list_evidence_sources. It does not explicitly state when not to use it or name alternative tools, but the context is unambiguous enough for an agent to select it correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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